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A new back-propagation neural network optimized with cuckoo search algorithm

Mohd Nawi, Nazri and Khan, Abdullah and Rehman, Mohammad Zubair (2013) A new back-propagation neural network optimized with cuckoo search algorithm. In: International Conference on Computational Science and Its Applications (ICCSA 2013), 24-27 June 2013, Ho Chi Minh City.

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Abstract

Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposed a new meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird’s behavior to train BP in achieving fast convergence rate and to avoid local minima problem. The performance of the proposed Cuckoo Search Back-Propagation (CSBP) is compared with artificial bee colony using BP algorithm, and other hybrid variants. Specifically OR and XOR datasets are used. The simulation results show that the computational efficiency of BP training process is highly enhanced when coupled with the proposed hybrid method.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: back propagation neural network; cuckoo search algorithm; local minima; and artificial bee colony algorithm
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 11 Jul 2013 02:46
Last Modified: 21 Jan 2015 08:07
URI: http://eprints.uthm.edu.my/id/eprint/4002
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